Evidence map›Paper›PMID 41219333›Full record

ArticleScientific reports2025

Structure-based identification of novel FAK1 inhibitors using pharmacophore modeling, molecular dynamics, and MM/PBSA calculations.

Amirhossein Hajipasha, Nilofar Ghaffari Cherati, Mohammad Darzi, Seyedeh Sana Nateghi, Seyed Arshia Sadat Mohsenian, Mahla Noorzaei, Fatemeh Kashiri Dinaki, Mohammad Halimi

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Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Amirhossein Hajipasha *Department of Biology, Babol Branch, Islamic Azad University, Babol, Iran.
Nilofar Ghaffari Cherati *Department of Biology, Babol Branch, Islamic Azad University, Babol, Iran.
Mohammad Darzi *Department of Biology, Babol Branch, Islamic Azad University, Babol, Iran.
Seyedeh Sana NateghiDepartment of Biology, Babol Branch, Islamic Azad University, Babol, Iran.
Seyed Arshia Sadat MohsenianDepartment of Biology, Babol Branch, Islamic Azad University, Babol, Iran.
Mahla NoorzaeiDepartment of Biology, Babol Branch, Islamic Azad University, Babol, Iran.
Fatemeh Kashiri DinakiDepartment of Biology, Babol Branch, Islamic Azad University, Babol, Iran.
Mohammad HalimiDepartment of Biology, Babol Branch, Islamic Azad University, Babol, Iran. halimi@baboliau.ac.ir.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Focal adhesion kinase 1 (FAK1) is a non-receptor tyrosine kinase involved in cancer metastasis and tumor progression. Due to its role in regulating cell migration and survival, FAK1 is considered a promising target for cancer therapy. While large chemical databases such as ZINC offer a wide variety of molecules, many of them have not yet been explored as potential FAK1 inhibitors.In this study, we applied several computational methods to identify new inhibitors of the FAK1 kinase domain. Pharmacophore models were built based on the FAK1-P4N complex (PDB ID: 6YOJ), and the most statistically reliable model was used to screen compounds from the ZINC database. Hits were first docked using AutoDock Vina in PyRx, and seventeen compounds with acceptable pharmacokinetic properties and low predicted toxicity were selected for more precise docking via SwissDock. Four promising candidates-ZINC23845603, ZINC44851809, ZINC266691666, and ZINC20267780-were chosen for molecular dynamics (MD) simulations using GROMACS. The stability and behavior of each protein-ligand complex were examined, and binding free energies were calculated using the MM/PBSA method. Among them, ZINC23845603 showed strong binding and interaction features similar to the known ligand P4N. Given its favorable binding energy and pharmacokinetic profile, ZINC23845603 may be a good candidate for further experimental studies targeting FAK1.

Indexed as

Focal Adhesion Kinase 1Molecular Dynamics SimulationProtein Kinase InhibitorsBinding SitesHumansLigandsMolecular Docking SimulationPharmacophoreProtein BindingFocal Adhesion Kinase 1LigandsProtein Kinase InhibitorsDockingFAK1 inhibitorMD simulationMM/PBSAPharmacophore modeling

Identifiers

PMID41219333
PMCPMC12606203

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